scholarly journals DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Clémentine Decamps ◽  
Alexis Arnaud ◽  
Florent Petitprez ◽  
Mira Ayadi ◽  
Aurélia Baurès ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Molecular Heterogeneity ◽  
Cell Type ◽  
Cancer Heterogeneity ◽  
Bioinformatic Tools ◽  
New Methods ◽  
Benchmark Datasets ◽  
User Friendly ◽  
Improved Methods

Abstract Background Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data. Results We present DECONbench, a standardized unbiased benchmarking resource, applied to the evaluation of computational methods quantifying cell-type heterogeneity in cancer. DECONbench includes gold standard simulated benchmark datasets, consisting of transcriptome and methylome profiles mimicking pancreatic adenocarcinoma molecular heterogeneity, and a set of baseline deconvolution methods (reference-free algorithms inferring cell-type proportions). DECONbench performs a systematic performance evaluation of each new methodological contribution and provides the possibility to publicly share source code and scoring. Conclusion DECONbench allows continuous submission of new methods in a user-friendly fashion, each novel contribution being automatically compared to the reference baseline methods, which enables crowdsourced benchmarking. DECONbench is designed to serve as a reference platform for the benchmarking of deconvolution methods in the evaluation of cancer heterogeneity. We believe it will contribute to leverage the benchmarking practices in the biomedical and life science communities. DECONbench is hosted on the open source Codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/27453.

scholarly journals DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

2020 ◽  
Author(s):  
Clémentine Decamps ◽  
Alexis Arnaud ◽  
Florent Petitprez ◽  
Mira Ayadi ◽  
Aurélia Baurès ◽  
...  
Keyword(s):  
Computational Methods ◽  
Tumor Heterogeneity ◽  
Supplementary Information ◽  
Web Based ◽  
Additional Information ◽  
New Methods ◽  
Link Type ◽  
Benchmark Datasets ◽  
Supplementary Material ◽  
And Performance

AbstractMotivationQuantification of tumor heterogeneity is essential to better understand cancer progressionand to adapt therapeutic treatments to patient specificities.ResultsWe present DECONbench, a web-based application to benchmark computational methods dedicated to quantify of cell-type heterogeneity in cancer. DECONbench includes benchmark datasets, computational methods and performance evaluation. It allows submission of new methods.Availability and implementationDECONbench is hosted on the open source codalab competition platform. It is freely available at: https://competitions.codalab.org/competitions/23660.Supplementary informationAdditional information is available online and on our website: https://cancer-heterogeneity.github.io/deconbench.html.

scholarly journals HDCytoData: Collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1459 ◽  
Author(s):  
Lukas M. Weber ◽  
Charlotte Soneson
Keyword(s):  
Method Development ◽  
Computational Analysis ◽  
High Dimensional ◽  
Bioconductor Package ◽  
Crucial Step ◽  
New Methods ◽  
The Future ◽  
Benchmark Datasets

Benchmarking is a crucial step during computational analysis and method development. Recently, a number of new methods have been developed for analyzing high-dimensional cytometry data. However, it can be difficult for analysts and developers to find and access well-characterized benchmark datasets. Here, we present HDCytoData, a Bioconductor package providing streamlined access to several publicly available high-dimensional cytometry benchmark datasets. The package is designed to be extensible, allowing new datasets to be contributed by ourselves or other researchers in the future. Currently, the package includes a set of experimental and semi-simulated datasets, which have been used in our previous work to evaluate methods for clustering and differential analyses. Datasets are formatted into standard SummarizedExperiment and flowSet Bioconductor object formats, which include complete metadata within the objects. Access is provided through Bioconductor's ExperimentHub interface. The package is freely available from http://bioconductor.org/packages/HDCytoData.

scholarly journals FIREcaller: Detecting Frequently Interacting Regions from Hi-C Data

2019 ◽  
Author(s):  
Cheynna Crowley ◽  
Yuchen Yang ◽  
Yunjiang Qiu ◽  
Benxia Hu ◽  
Armen Abnousi ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Spatial Organization ◽  
R Package ◽  
Specific Gene ◽  
List Type ◽  
Cell Type ◽  
R Software ◽  
Computational Tools ◽  
Cell Type Specific ◽  
User Friendly

AbstractHi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller.Highlights– Frequently Interacting Regions (FIREs) can be used to identify tissue and cell-type-specific cis-regulatory regions.– An R software, FIREcaller, has been developed to identify FIREs and clustered FIREs into super-FIREs.

LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor

2019 ◽  
Vol 21 (6) ◽  
pp. 2206-2218 ◽  
Author(s):  
Jing-Fang Yang ◽  
Fan Wang ◽  
Yu-Zong Chen ◽  
Ge-Fei Hao ◽  
Guang-Fu Yang
Keyword(s):  
Estrogen Receptor ◽  
Protein Dynamics ◽  
Protein Function ◽  
Inflammatory Diseases ◽  
Great Difficulty ◽  
Vital Role ◽  
Site Directed Mutagenesis ◽  
Bioinformatic Tools ◽  
Wide Range ◽  
User Friendly

Abstract Protein dynamics is central to all biological processes, including signal transduction, cellular regulation and biological catalysis. Among them, in-depth exploration of ligand-driven protein dynamics contributes to an optimal understanding of protein function, which is particularly relevant to drug discovery. Hence, a wide range of computational tools have been designed to investigate the important dynamic information in proteins. However, performing and analyzing protein dynamics is still challenging due to the complicated operation steps, giving rise to great difficulty, especially for nonexperts. Moreover, there is a lack of web protocol to provide online facility to investigate and visualize ligand-driven protein dynamics. To this end, in this study, we integrated several bioinformatic tools to develop a protocol, named Ligand and Receptor Molecular Dynamics (LARMD, http://chemyang.ccnu.edu.cn/ccb/server/LARMD/ and http://agroda.gzu.edu.cn:9999/ccb/server/LARMD/), for profiling ligand-driven protein dynamics. To be specific, estrogen receptor (ER) was used as a case to reveal ERβ-selective mechanism, which plays a vital role in the treatment of inflammatory diseases and many types of cancers in clinical practice. Two different residues (Ile373/Met421 and Met336/Leu384) in the pocket of ERβ/ERα were the significant determinants for selectivity, especially Met336 of ERβ. The helix H8, helix H11 and H7-H8 loop influenced the migration of selective agonist (WAY-244). These computational results were consistent with the experimental results. Therefore, LARMD provides a user-friendly online protocol to study the dynamic property of protein and to design new ligand or site-directed mutagenesis.

scholarly journals SEVA 3.0: an update of the Standard European Vector Architecture for enabling portability of genetic constructs among diverse bacterial hosts

2019 ◽  
Vol 48 (D1) ◽  
pp. D1164-D1170 ◽  
Author(s):  
Esteban Martínez-García ◽  
Angel Goñi-Moreno ◽  
Bryan Bartley ◽  
James McLaughlin ◽  
Lucas Sánchez-Sampedro ◽  
...  
Keyword(s):  
Synthetic Biology ◽  
Bacterial Species ◽  
Web Based ◽  
Genomic Engineering ◽  
Bioinformatic Tools ◽  
Synthetic Biology Open Language ◽  
User Friendly ◽  
Machine Readable ◽  
Genetic Constructs

Abstract The Standard European Vector Architecture 3.0 database (SEVA-DB 3.0, http://seva.cnb.csic.es) is the update of the platform launched in 2013 both as a web-based resource and as a material repository of formatted genetic tools (mostly plasmids) for analysis, construction and deployment of complex bacterial phenotypes. The period between the first version of SEVA-DB and the present time has witnessed several technical, computational and conceptual advances in genetic/genomic engineering of prokaryotes that have enabled upgrading of the utilities of the updated database. Novelties include not only a more user-friendly web interface and many more plasmid vectors, but also new links of the plasmids to advanced bioinformatic tools. These provide an intuitive visualization of the constructs at stake and a range of virtual manipulations of DNA segments that were not possible before. Finally, the list of canonical SEVA plasmids is available in machine-readable SBOL (Synthetic Biology Open Language) format. This ensures interoperability with other platforms and affords simulations of their behaviour under different in vivo conditions. We argue that the SEVA-DB will remain a useful resource for extending Synthetic Biology approaches towards non-standard bacterial species as well as genetically programming new prokaryotic chassis for a suite of fundamental and biotechnological endeavours.

scholarly journals Bladder Tumor Heterogeneity: The Impact on Clinical Treatment

2015 ◽  
Vol 95 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Chao Chen ◽  
Xiang Jie Qi ◽  
Yan Wei Cao ◽  
Yong Hua Wang ◽  
Xue Cheng Yang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Treatment Failure ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Bladder Tumor ◽  
Gene Sequencing ◽  
Generation Model ◽  
And Control ◽  
The Impact

Bladder cancer relapse and treatment failure in most patients have often been attributed to chemoresistance in tumor cells and metastasis. Emerging evidence indicates that tumor heterogeneity may play an equally important role and extends to virtually all measurable properties of cancer cells. Although the idea of tumor heterogeneity is not new, little attention has been paid to applying it to understand and control bladder cancer progression. With the development of biotechnology, such as Gene sequencing, recent advances in understanding its generation model, original basis, consequent problems, and derived therapies provide great potential for tumor heterogeneity to be considered a new insight in the treatment of bladder cancers.

scholarly journals Accounting for tumor heterogeneity when using CRISPR-Cas9 for cancer progression and drug sensitivity studies

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198790 ◽  
Author(s):  
Jessica F. Olive ◽  
Yuanbo Qin ◽  
Molly J. DeCristo ◽  
Tyler Laszewski ◽  
Frances Greathouse ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Drug Sensitivity ◽  
Sensitivity Studies

scholarly journals Roles of GPRC5 family proteins: focusing on GPRC5B and lipid-mediated signalling

2020 ◽  
Vol 167 (6) ◽  
pp. 541-547 ◽  
Author(s):  
Yoshio Hirabayashi ◽  
Yeon-Jeong Kim
Keyword(s):  
Cancer Progression ◽  
Knockout Mouse ◽  
Critical Role ◽  
The Body ◽  
Sphingolipid Metabolism ◽  
Gene Products ◽  
Cell Type ◽  
The Past ◽  
Cell Type Specific ◽  
And Control

Abstract In the past decade, physiological roles and molecular functions of GPRC5 family receptors, originally identified as retinoic acid-induced gene products, have been uncovered, even though their intrinsic agonists are still a mystery. They are differentially distributed in certain tissues and cells in the body suggesting that cell-type-specific regulations and functions are significant. Molecular biological approaches and knockout mouse studies reveal that GPRC5 family proteins have pivotal roles in cancer progression and control of metabolic homeostasis pathways. Remarkably, GPRC5B-mediated tyrosine-phosphorylation signalling cascades play a critical role in development of obesity and insulin resistance through dynamic sphingolipid metabolism.

scholarly journals afpCOOL: An Accurate Tool for Antifreeze Protein Detection

2017 ◽  
Author(s):  
Morteza Eslami ◽  
Ramin Shirali-hossein-zade ◽  
Zeinab Takalloo ◽  
Ghasem Mahdevar ◽  
Abbasali Emamjomeh ◽  
...  
Keyword(s):  
Protein Detection ◽  
Freeze Tolerance ◽  
Support Vector ◽  
Discrimination Power ◽  
Current State ◽  
Comparison Results ◽  
Evolutionary Features ◽  
Benchmark Datasets ◽  
Cold Hardy ◽  
User Friendly

ABSTRACTVarious cold-adapted organisms produce antifreeze proteins (AFPs), which prevent to freeze of cell fluids by resisting the growth of the ice crystal. AFPs are currently being recognized in various organisms that are living in extremely low temperatures. AFPs have several important applications in increasing freeze tolerance of plants; maintain the tissue in frozen conditions and producing cold-hardy plants using transgenic technology. Substantial differences in the sequence and structure of the AFPs, pose a challenge for researcher to identify these proteins. In this paper, we proposed a novel method for identifying AFPs using support vector machine (SVM) by incorporating 4 types of features. Results on two benchmark datasets revealed the strength of the proposed method in AFP prediction. Also, according to the results on an independent test set, our method outperformed the current state-of-the-art methods. The further analysis showed the non-satisfactory performance of the BLAST in AFP detection: more than 62% of the BLAST searches have specificity less than 10% and there is no any BLAST search with sensitivity higher than 10%. These results reveal the urgent need for an accurate tool for AFP detection. In addition, the comparison results of the discrimination power of different feature types disclosed that evolutionary features and amino acid composition are the most contributing features in AFP detection. This method has been implemented as a stand-alone tool, namely afpCOOL, for various operating systems to predict AFPs with a user friendly graphical interface.AvailabilityafpCOOL is freely available at http://bioinf.modares.ac.ir:8080/AFPCOOL/page/afpcool.ispContactDr Zahiri [email protected]

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